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Chaos-Enhanced Grey Wolf Optimization for Fractional-Order PID Control of a DC Motor System | ||
| International Journal of Industrial Electronics Control and Optimization | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 27 مرداد 1405 اصل مقاله (578.68 K) | ||
| نوع مقاله: Research Articles | ||
| شناسه دیجیتال (DOI): 10.22111/ieco.2026.54676.1745 | ||
| نویسندگان | ||
| Maryam Khalili Fard1؛ Ali Hatami* 2 | ||
| 1Faculty of Mathematics, University of Sistan and Baluchestan, Zahedan, Iran | ||
| 2University Of Sistan and Baluchestan | ||
| چکیده | ||
| Fractional-order PID (FOPID) controllers offer enhanced flexibility and robustness compared to classical integer-order PID controllers, making them well suited for complex and nonlinear control systems. However, the optimal tuning of FOPID parameters remains a challenging task due to the increased number of tuning parameters and the nonlinear nature of the resulting optimization problem. In this paper, a chaos-enhanced Grey Wolf Optimization (CGWO) algorithm is proposed for the optimal tuning of a FOPID controller applied to a DC motor system. Chaotic maps are integrated into the standard Grey Wolf Optimizer to improve population diversity, enhance global search capability, and mitigate premature convergence. Logistic and Tent chaotic maps are employed to dynamically regulate the control parameters of the optimizer throughout the optimization process. The proposed CGWO algorithm is implemented in a MATLAB/Simulink environment, and the controller performance is evaluated using the Integral of Time-Weighted Absolute Error (ITAE) as the optimization objective. Simulation results demonstrate that the proposed CGWO-FOPID controller outperforms both the conventional PID controller and the standard GWO-based FOPID controller in terms of transient response, convergence behavior, and control accuracy. Specifically, reduced overshoot, shorter settling time, and lower ITAE values are achieved. These results confirm that incorporating chaotic dynamics into metaheuristic optimization significantly enhances the tuning performance of fractional-order controllers and provides an effective and practical solution for industrial control applications. | ||
| کلیدواژهها | ||
| Chaos theory؛ Fractional-order control؛ Grey Wolf Optimizer؛ Metaheuristic optimization؛ PID control | ||
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آمار تعداد مشاهده مقاله: 12 تعداد دریافت فایل اصل مقاله: 49 |
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